4180 Chattanooga Crime Data
Thomas Raines, Yatri Patel
10/15/2021
Libraries ————————————————————————————————
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
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## x dplyr::lag() masks stats::lag()library(ggplot2)
library(dplyr)
library(reshape2)##
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## smithslibrary(lubridate)##
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## date, intersect, setdiff, unionlibrary(sf)## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1library(raster)## Loading required package: sp##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
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## selectlibrary(ggplot2)
library(incidence)
library(plotly)##
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## last_plot## The following object is masked from 'package:stats':
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## layoutRead the main police incident database
#
police_incident <- read_csv("Police_Incident_Data.csv")## Rows: 419836 Columns: 19## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (16): Address, City, State, Zip, Date_Incident, Jurisdiction, Incident_T...
## dbl (3): Latitude, Longitude, citydst##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(police_incident)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1200 BROAD~ Chatt~ TN 3740~ 01/01/2015 1~ TN0330100 A9 Adam~
## 2 500 W 12TH~ Chatt~ TN 3740~ 01/01/2015 1~ TN0330100 A6 Adam~
## 3 1000 Sheri~ Chatt~ TN 3740~ 01/01/2015 1~ TN0330100 B3 Bake~
## 4 1600 OAK ST Chatt~ TN 3740~ 01/01/2015 1~ TN0330100 B4 Bake~
## 5 4000 Blanc~ Chatt~ TN 3741~ 01/01/2015 1~ TN0330100 C2 Char~
## 6 8300 Grind~ CHATT~ TN 3742~ 01/01/2015 1~ TN0330100 C7 Char~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Read the tract populations dataframe
censusPopData <- read_csv('Census_2020_Tract_Populations.csv')## Rows: 1701 Columns: 26## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (4): County FIPS Code, Census Tract, Common Name, geometry
## dbl (22): State FIPS Code, GeoID, Total Population, Population of One Race, ...##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(censusPopData)## # A tibble: 6 x 26
## `State FIPS Code` `County FIPS Code` `Census Tract` GeoID `Common Name`
## <dbl> <chr> <chr> <dbl> <chr>
## 1 47 175 925200 47175925200 Census Tract ~
## 2 47 175 925000 47175925000 Census Tract ~
## 3 47 003 950201 47003950201 Census Tract ~
## 4 47 003 950202 47003950202 Census Tract ~
## 5 47 093 003300 47093003300 Census Tract ~
## 6 47 093 002100 47093002100 Census Tract ~
## # ... with 21 more variables: Total Population <dbl>,
## # Population of One Race <dbl>, White Alone <dbl>,
## # Black or African American Alone <dbl>,
## # American Indian and Alaska Native Alone <dbl>, Asian Alone <dbl>,
## # Native Hawaiian and Other Pacific Islander Alone <dbl>,
## # Some Other Race Alone <dbl>, Population of Two or More Races <dbl>,
## # Hispanic or Latino <dbl>, Not Hispanic or Latino <dbl>, geometry <chr>, ...colnames(censusPopData)[3] <- "TRACT_ID"Make a DataFrame for Income data
censusIncData <- read_csv('2020_Median_Income.csv')## Rows: 82 Columns: 7## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (3): COUNTY_FIP, TRACT_ID, geom
## dbl (1): STATE_FIP##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(censusIncData)## # A tibble: 6 x 7
## STATE_FIP COUNTY_FIP TRACT_ID `Median Income` `Median Income Male`
## <dbl> <chr> <chr> <dbl> <dbl>
## 1 47 065 001300 17175 19924
## 2 47 065 000400 16360 17588
## 3 47 065 010502 27248 37325
## 4 47 065 002400 15523 15567
## 5 47 065 010432 40555 55556
## 6 47 065 011600 22442 24060
## # ... with 2 more variables: Median Income Female <dbl>, geom <chr>Merge the income and population data by Tract ID
#
censusData <- merge(censusPopData,censusIncData,by="TRACT_ID")
head(censusData)## TRACT_ID State FIPS Code County FIPS Code GeoID Common Name
## 1 000400 47 113 4.7113e+10 Census Tract 4
## 2 000400 47 065 4.7065e+10 Census Tract 4
## 3 000400 47 141 4.7141e+10 Census Tract 4
## 4 000400 47 157 4.7157e+10 Census Tract 4
## 5 000600 47 065 4.7065e+10 Census Tract 6
## 6 000600 47 113 4.7113e+10 Census Tract 6
## Total Population Population of One Race White Alone
## 1 3603 3494 717
## 2 3002 2914 334
## 3 4186 3964 3688
## 4 1350 1319 15
## 5 3620 3429 3193
## 6 1833 1751 752
## Black or African American Alone American Indian and Alaska Native Alone
## 1 2672 10
## 2 2513 10
## 3 63 33
## 4 1299 2
## 5 97 12
## 6 976 0
## Asian Alone Native Hawaiian and Other Pacific Islander Alone
## 1 15 0
## 2 12 0
## 3 72 1
## 4 0 0
## 5 72 0
## 6 3 2
## Some Other Race Alone Population of Two or More Races Hispanic or Latino
## 1 80 109 116
## 2 45 88 81
## 3 107 222 231
## 4 3 31 16
## 5 55 191 131
## 6 18 82 54
## Not Hispanic or Latino
## 1 3487
## 2 2921
## 3 3955
## 4 1334
## 5 3489
## 6 1779
## geometry
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## 4 MULTIPOLYGON (((-90.02512 35.16874, -90.02509 35.169693, -90.02506 35.17103, -90.02505 35.17179, -90.02504 35.171925, -90.02503 35.172325, -90.02503 35.17246, -90.025024 35.172607, -90.02502 35.17305, -90.02501 35.1732, -90.025 35.17347, -90.02499 35.174286, -90.02498 35.174557, -90.02485 35.17455, -90.02446 35.17453, -90.02433 35.174522, -90.02427 35.175426, -90.02424 35.17627, -90.02414 35.17813, -90.02413 35.17834, -90.0241 35.179035, -90.02406 35.17957, -90.02406 35.179585, -90.024025 35.17985, -90.023994 35.18049, -90.02401 35.18057, -90.02402 35.180603, -90.02405 35.180683, -90.0241 35.180744, -90.02425 35.180836, -90.02441 35.180866, -90.02447 35.180866, -90.024506 35.180866, -90.024605 35.180893, -90.0247 35.180973, -90.02472 35.18101, -90.02472 35.181236, -90.02472 35.18131, -90.02471 35.18152, -90.02471 35.18159, -90.024666 35.18169, -90.02452 35.181995, -90.024475 35.182095, -90.0242 35.18215, -90.02338 35.18232, -90.0231 35.182377, -90.022 35.182713, -90.02164 35.182823, -90.020805 35.18312, -90.01964 35.183853, -90.019 35.184334, -90.01842 35.184776, -90.01807 35.184994, -90.01804 35.18501, -90.01798 35.18505, -90.01796 35.185066, -90.01796 35.185116, -90.01796 35.185383, -90.01796 35.186054, -90.01795 35.18634, -90.01795 35.18666, -90.01795 35.18668, -90.01795 35.18674, -90.01795 35.18676, -90.01793 35.186756, -90.01787 35.18674, -90.017845 35.186737, -90.01701 35.18675, -90.01577 35.186737, -90.01529 35.186737, -90.01504 35.18674, -90.01392 35.186344, -90.01325 35.186356, -90.01191 35.18686, -90.01003 35.18725, -90.009315 35.1874, -90.008514 35.187622, -90.008095 35.18772, -90.008095 35.187645, -90.008095 35.18749, -90.0081 35.18727, -90.0081 35.187256, -90.00813 35.186214, -90.0082 35.182365, -90.00821 35.182262, -90.00821 35.18192, -90.008224 35.181217, -90.00831 35.178116, -90.00831 35.178078, -90.00834 35.177032, -90.00835 35.17677, -90.00835 35.17676, -90.00837 35.175976, -90.00838 35.175713, -90.008385 35.175438, -90.0084 35.17461, -90.00841 35.174335, -90.008415 35.17408, -90.008446 35.173306, -90.00845 35.173084, -90.00845 35.173046, -90.00846 35.172974, -90.00847 35.172787, -90.00847 35.17272, -90.00885 35.172733, -90.00946 35.17276, -90.009995 35.17276, -90.010376 35.17276, -90.010666 35.17277, -90.01075 35.17277, -90.011536 35.172775, -90.011826 35.17278, -90.01214 35.172714, -90.01224 35.172688, -90.0126 35.172592, -90.01308 35.17246, -90.01345 35.172337, -90.01355 35.172306, -90.013855 35.172195, -90.01411 35.1721, -90.01487 35.171814, -90.01513 35.17172, -90.01538 35.171623, -90.01615 35.171333, -90.0164 35.171238, -90.01667 35.17114, -90.01718 35.17095, -90.01747 35.17084, -90.01774 35.170742, -90.01793 35.17067, -90.01836 35.17051, -90.01838 35.1705, -90.01845 35.17048, -90.01853 35.17045, -90.01864 35.170406, -90.018974 35.170288, -90.01909 35.170246, -90.01921 35.1702, -90.01954 35.170082, -90.0196 35.17006, -90.01964 35.170048, -90.01973 35.170013, -90.01981 35.16998, -90.02004 35.16989, -90.020096 35.169876, -90.02019 35.169846, -90.02049 35.169743, -90.02072 35.169666, -90.021385 35.16941, -90.02168 35.169296, -90.02183 35.16924, -90.02228 35.16907, -90.02243 35.169014, -90.0229 35.168835, -90.023964 35.168434, -90.02404 35.168407, -90.02406 35.1684, -90.024864 35.16809, -90.02515 35.167976, -90.02512 35.16874)))
## 5 MULTIPOLYGON (((-85.30923 35.058823, -85.30905 35.059742, -85.30901 35.059914, -85.30899 35.06005, -85.30889 35.06057, -85.30868 35.061672, -85.30859 35.062122, -85.30852 35.062553, -85.30852 35.06264, -85.30856 35.062984, -85.30857 35.063038, -85.308815 35.064213, -85.30886 35.064404, -85.30891 35.0646, -85.30895 35.064762, -85.309 35.06495, -85.309044 35.065247, -85.309044 35.06526, -85.30906 35.065414, -85.30906 35.065422, -85.30906 35.06552, -85.30906 35.065777, -85.30907 35.06654, -85.309074 35.066795, -85.309044 35.067093, -85.30902 35.06743, -85.30896 35.067986, -85.30893 35.068283, -85.30889 35.06866, -85.30878 35.06979, -85.30874 35.070168, -85.308716 35.070385, -85.3087 35.07055, -85.30874 35.070614, -85.30871 35.070957, -85.3087 35.071026, -85.30868 35.071243, -85.30858 35.071404, -85.30851 35.071514, -85.30845 35.07158, -85.30836 35.071655, -85.30816 35.07178, -85.308105 35.071815, -85.308 35.071877, -85.307915 35.071953, -85.307526 35.072166, -85.30741 35.072227, 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## 6 MULTIPOLYGON (((-88.826385 35.629, -88.82638 35.629044, -88.82635 35.62909, -88.82627 35.629128, -88.82623 35.629158, -88.826225 35.629173, -88.82619 35.629223, -88.82617 35.629253, -88.82616 35.62931, -88.82616 35.629387, -88.82616 35.629498, -88.82617 35.62957, -88.82618 35.629627, -88.826294 35.629925, -88.826324 35.630047, -88.82633 35.630096, -88.82635 35.630276, -88.82636 35.630447, -88.82634 35.630936, -88.826324 35.63108, -88.82631 35.63147, -88.82629 35.631844, -88.82624 35.632835, -88.82623 35.633026, -88.8262 35.633614, -88.826195 35.63381, -88.82619 35.633877, -88.82618 35.634087, -88.82618 35.634155, -88.82617 35.634415, -88.82615 35.635155, -88.82615 35.635193, -88.82614 35.635452, -88.82604 35.63544, -88.82583 35.635433, -88.82552 35.635426, -88.824905 35.635418, -88.82459 35.635414, -88.8244 35.63541, -88.82424 35.635406, -88.82412 35.635395, -88.82384 35.63534, -88.823654 35.635303, -88.823586 35.635292, -88.82337 35.635254, -88.8233 35.635242, -88.82294 35.635216, -88.82289 35.635216, -88.822464 35.635197, -88.82204 35.635193, -88.82184 35.635185, -88.82147 35.635174, -88.82122 35.635166, -88.82086 35.635155, -88.82047 35.63513, -88.82022 35.63512, -88.82007 35.63511, -88.819984 35.635105, -88.81971 35.6351, -88.819595 35.63511, -88.819435 35.635124, -88.81934 35.635128, -88.81894 35.635117, -88.818695 35.63511, -88.8184 35.63509, -88.81801 35.635082, -88.817444 35.63507, -88.81694 35.63506, -88.8167 35.63505, -88.81598 35.635033, -88.81574 35.63503, -88.81493 35.635006, -88.81433 35.634987, -88.81283 35.63494, -88.81266 35.634933, -88.81214 35.63492, -88.811966 35.634914, -88.811714 35.634907, -88.81141 35.634895, -88.81095 35.63488, -88.8107 35.634872, -88.81032 35.634857, -88.809715 35.63484, -88.80956 35.63483, -88.80926 35.634804, -88.80917 35.6348, -88.80905 35.6348, -88.80886 35.634804, -88.80883 35.634796, -88.80879 35.634785, -88.808784 35.634796, -88.808754 35.63483, -88.80875 35.634842, -88.808716 35.63484, -88.808685 35.634834, -88.80863 35.63483, -88.8086 35.634834, -88.80859 35.63484, -88.80853 35.634853, -88.80818 35.634895, -88.808075 35.63489, -88.807846 35.634872, -88.80768 35.634857, -88.80703 35.63481, -88.80671 35.63478, -88.80582 35.634716, -88.80534 35.63469, -88.80489 35.634655, -88.80418 35.6346, -88.803986 35.63458, -88.80396 35.634575, -88.80422 35.63406, -88.804924 35.63267, -88.80507 35.63241, -88.80499 35.63239, -88.80497 35.63238, -88.80498 35.632355, -88.80527 35.632156, -88.80547 35.63201, -88.8057 35.631847, -88.80605 35.631554, -88.806366 35.631268, -88.80663 35.631012, -88.80675 35.630894, -88.80685 35.63078, -88.807106 35.630463, -88.80732 35.63018, -88.807785 35.6295, -88.808304 35.628727, -88.808815 35.627968, -88.808975 35.627743, -88.809 35.62774, -88.809074 35.62774, -88.8091 35.62774, -88.809525 35.627766, -88.809784 35.62778, -88.81036 35.627823, -88.810905 35.627872, -88.81174 35.62795, -88.81181 35.62796, -88.81184 35.627968, -88.81197 35.628002, -88.812096 35.628025, -88.81252 35.628056, -88.81268 35.62806, -88.81271 35.628063, -88.81277 35.628063, -88.81281 35.628067, -88.81286 35.62807, -88.81303 35.628056, -88.813156 35.628033, -88.81332 35.62801, -88.813385 35.628, -88.81343 35.62799, -88.81354 35.62798, -88.81358 35.62798, -88.813614 35.627975, -88.813644 35.627975, -88.81386 35.62798, -88.81387 35.62798, -88.81442 35.628, -88.81471 35.62801, -88.81557 35.62804, -88.81684 35.628082, -88.81705 35.62809, -88.81768 35.628113, -88.81777 35.628117, -88.817894 35.628117, -88.81807 35.628113, -88.81837 35.628105, -88.81862 35.62811, -88.8188 35.62811, -88.819 35.62811, -88.81915 35.628113, -88.820206 35.628136, -88.82056 35.628143, -88.82081 35.628147, -88.82155 35.62816, -88.82179 35.628162, -88.822044 35.628166, -88.82279 35.62818, -88.823044 35.62819, -88.82329 35.62819, -88.82345 35.628193, -88.824005 35.628204, -88.824165 35.62821, -88.82424 35.628193, -88.824295 35.628178, -88.824326 35.628174, -88.82643 35.628273, -88.826385 35.629)))
## Black or African American Alone Percent White Alone Percent
## 1 0.7416 0.1990
## 2 0.8371 0.1113
## 3 0.0151 0.8810
## 4 0.9622 0.0111
## 5 0.0268 0.8820
## 6 0.5325 0.4103
## American Indian and Alaska Native Alone Percent Asian Alone Percent
## 1 0.0028 0.0042
## 2 0.0033 0.0040
## 3 0.0079 0.0172
## 4 0.0015 0.0000
## 5 0.0033 0.0199
## 6 0.0000 0.0016
## Native Hawaiian and Other Pacific Islander Alone Percent
## 1 0.0000
## 2 0.0000
## 3 0.0002
## 4 0.0000
## 5 0.0000
## 6 0.0011
## Some Other Race Alone Percent Two or More Races Percent
## 1 0.0222 0.0303
## 2 0.0150 0.0293
## 3 0.0256 0.0530
## 4 0.0022 0.0230
## 5 0.0152 0.0528
## 6 0.0098 0.0447
## Hispanic or Latino Percent Not Hispanic or Latino Percent STATE_FIP
## 1 0.0322 0.9678 47
## 2 0.0270 0.9730 47
## 3 0.0552 0.9448 47
## 4 0.0119 0.9881 47
## 5 0.0362 0.9638 47
## 6 0.0295 0.9705 47
## COUNTY_FIP Median Income Median Income Male Median Income Female
## 1 065 16360 17588 15394
## 2 065 16360 17588 15394
## 3 065 16360 17588 15394
## 4 065 16360 17588 15394
## 5 065 35261 50172 29048
## 6 065 35261 50172 29048
## geom
## 1 POLYGON ((-85.297859 35.050803, -85.297517 35.050962, -85.294737 35.052119999999995, -85.288797 35.052296999999996, -85.287407 35.052855, -85.285236 35.054224999999995, -85.277542 35.057095, -85.27574899999999 35.058548, -85.271298 35.056262, -85.270144 35.055761, -85.26898899999999 35.055588, -85.27054799999999 35.054656, -85.26574699999999 35.052475, -85.26173399999999 35.050976, -85.263077 35.04855, -85.263244 35.046558, -85.264579 35.043529, -85.267066 35.039041, -85.26922499999999 35.039871, -85.271985 35.040877, -85.286733 35.046303, -85.286887 35.046389, -85.288721 35.045988, -85.291009 35.046856999999996, -85.293617 35.047824999999996, -85.29312999999999 35.048738, -85.29604499999999 35.050125, -85.296266 35.050188999999996, -85.297615 35.050699, -85.298029 35.050759, -85.297859 35.050803))
## 2 POLYGON ((-85.297859 35.050803, -85.297517 35.050962, -85.294737 35.052119999999995, -85.288797 35.052296999999996, -85.287407 35.052855, -85.285236 35.054224999999995, -85.277542 35.057095, -85.27574899999999 35.058548, -85.271298 35.056262, -85.270144 35.055761, -85.26898899999999 35.055588, -85.27054799999999 35.054656, -85.26574699999999 35.052475, -85.26173399999999 35.050976, -85.263077 35.04855, -85.263244 35.046558, -85.264579 35.043529, -85.267066 35.039041, -85.26922499999999 35.039871, -85.271985 35.040877, -85.286733 35.046303, -85.286887 35.046389, -85.288721 35.045988, -85.291009 35.046856999999996, -85.293617 35.047824999999996, -85.29312999999999 35.048738, -85.29604499999999 35.050125, -85.296266 35.050188999999996, -85.297615 35.050699, -85.298029 35.050759, -85.297859 35.050803))
## 3 POLYGON ((-85.297859 35.050803, -85.297517 35.050962, -85.294737 35.052119999999995, -85.288797 35.052296999999996, -85.287407 35.052855, -85.285236 35.054224999999995, -85.277542 35.057095, -85.27574899999999 35.058548, -85.271298 35.056262, -85.270144 35.055761, -85.26898899999999 35.055588, -85.27054799999999 35.054656, -85.26574699999999 35.052475, -85.26173399999999 35.050976, -85.263077 35.04855, -85.263244 35.046558, -85.264579 35.043529, -85.267066 35.039041, -85.26922499999999 35.039871, -85.271985 35.040877, -85.286733 35.046303, -85.286887 35.046389, -85.288721 35.045988, -85.291009 35.046856999999996, -85.293617 35.047824999999996, -85.29312999999999 35.048738, -85.29604499999999 35.050125, -85.296266 35.050188999999996, -85.297615 35.050699, -85.298029 35.050759, -85.297859 35.050803))
## 4 POLYGON ((-85.297859 35.050803, -85.297517 35.050962, -85.294737 35.052119999999995, -85.288797 35.052296999999996, -85.287407 35.052855, -85.285236 35.054224999999995, -85.277542 35.057095, -85.27574899999999 35.058548, -85.271298 35.056262, -85.270144 35.055761, -85.26898899999999 35.055588, -85.27054799999999 35.054656, -85.26574699999999 35.052475, -85.26173399999999 35.050976, -85.263077 35.04855, -85.263244 35.046558, -85.264579 35.043529, -85.267066 35.039041, -85.26922499999999 35.039871, -85.271985 35.040877, -85.286733 35.046303, -85.286887 35.046389, -85.288721 35.045988, -85.291009 35.046856999999996, -85.293617 35.047824999999996, -85.29312999999999 35.048738, -85.29604499999999 35.050125, -85.296266 35.050188999999996, -85.297615 35.050699, -85.298029 35.050759, -85.297859 35.050803))
## 5 POLYGON ((-85.308515 35.062552, -85.308515 35.062643, -85.309058 35.065413, -85.308679 35.071244, -85.307913 35.071951, -85.306185 35.07281, -85.304405 35.075759999999995, -85.301976 35.077655, -85.30068899999999 35.077225999999996, -85.300151 35.076139999999995, -85.300161 35.073150999999996, -85.299769 35.072817, -85.29659099999999 35.071867, -85.294913 35.068183, -85.294949 35.066486999999995, -85.293039 35.065709, -85.292656 35.06494, -85.28965199999999 35.065906999999996, -85.287048 35.065722, -85.285732 35.060871999999996, -85.286501 35.058415, -85.283993 35.057666, -85.285619 35.056745, -85.28876799999999 35.055786999999995, -85.295484 35.055648, -85.296787 35.056307, -85.302534 35.057368, -85.30554099999999 35.057699, -85.307256 35.058321, -85.309283 35.058513999999995, -85.308515 35.062552))
## 6 POLYGON ((-85.308515 35.062552, -85.308515 35.062643, -85.309058 35.065413, -85.308679 35.071244, -85.307913 35.071951, -85.306185 35.07281, -85.304405 35.075759999999995, -85.301976 35.077655, -85.30068899999999 35.077225999999996, -85.300151 35.076139999999995, -85.300161 35.073150999999996, -85.299769 35.072817, -85.29659099999999 35.071867, -85.294913 35.068183, -85.294949 35.066486999999995, -85.293039 35.065709, -85.292656 35.06494, -85.28965199999999 35.065906999999996, -85.287048 35.065722, -85.285732 35.060871999999996, -85.286501 35.058415, -85.283993 35.057666, -85.285619 35.056745, -85.28876799999999 35.055786999999995, -85.295484 35.055648, -85.296787 35.056307, -85.302534 35.057368, -85.30554099999999 35.057699, -85.307256 35.058321, -85.309283 35.058513999999995, -85.308515 35.062552))Make shape file for Census Population
##
censusPopShp <- read_sf("Census 2020 Tract Populations/geo_export_6ea9b8b9-f93b-41bd-8242-39b59cd81ddc.shp")
head(censusPopShp)## Simple feature collection with 6 features and 25 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -86.66377 ymin: 35.53276 xmax: -83.82455 ymax: 36.01498
## Geodetic CRS: WGS84(DD)
## # A tibble: 6 x 26
## state_fips county_fip census_tra geoid common_nam total_popu population
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 47 175 925200 47175925200 Census Tra~ 3095 3009
## 2 47 175 925000 47175925000 Census Tra~ 3073 2931
## 3 47 003 950201 47003950201 Census Tra~ 3368 3199
## 4 47 003 950202 47003950202 Census Tra~ 3991 3844
## 5 47 093 003300 47093003300 Census Tra~ 2147 2021
## 6 47 093 002100 47093002100 Census Tra~ 2857 2612
## # ... with 19 more variables: white_alon <dbl>, black_or_a <dbl>,
## # american_i <dbl>, asian_alon <dbl>, native_haw <dbl>, some_other <dbl>,
## # populati_2 <dbl>, hispanic_o <dbl>, not_hispan <dbl>, black_or_2 <dbl>,
## # white_al_2 <dbl>, american_2 <dbl>, asian_al_2 <dbl>, native_h_2 <dbl>,
## # some_oth_2 <dbl>, two_or_mor <dbl>, hispanic_2 <dbl>, not_hisp_2 <dbl>,
## # geometry <MULTIPOLYGON [°]>Make shape file for 2017 Median Income
##
censusIncShp <- read_sf("2020 Median Income/geo_export_43a087a5-8ff8-4733-9a99-dd658a9c17f4.shp")
censusIncShp <- censusIncShp %>%
rename(tract = tract_id)
head(censusIncShp)## Simple feature collection with 6 features and 6 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -85.30035 ymin: 34.9848 xmax: -85.16977 ymax: 35.15406
## Geodetic CRS: WGS84(DD)
## # A tibble: 6 x 7
## state_fip county_fip tract median_inc median_i_2 median_i_3
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 47 065 001300 17175 19924 15556
## 2 47 065 000400 16360 17588 15394
## 3 47 065 010502 27248 37325 18496
## 4 47 065 002400 15523 15567 15480
## 5 47 065 010432 40555 55556 30070
## 6 47 065 011600 22442 24060 17337
## # ... with 1 more variable: geometry <POLYGON [°]>Merge our Shape Files to one big shape file
##
sf::sf_use_s2(FALSE)## Spherical geometry (s2) switched offcensusDataShp <- st_join(censusIncShp, censusPopShp)## although coordinates are longitude/latitude, st_intersects assumes that they are planarhead(censusDataShp)## Simple feature collection with 6 features and 31 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -85.27716 ymin: 35.02097 xmax: -85.25746 ymax: 35.03486
## Geodetic CRS: WGS84(DD)
## # A tibble: 6 x 32
## state_fip county_fip.x tract median_inc median_i_2 median_i_3
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 47 065 001300 17175 19924 15556
## 2 47 065 001300 17175 19924 15556
## 3 47 065 001300 17175 19924 15556
## 4 47 065 001300 17175 19924 15556
## 5 47 065 001300 17175 19924 15556
## 6 47 065 001300 17175 19924 15556
## # ... with 26 more variables: geometry <POLYGON [°]>, state_fips <chr>,
## # county_fip.y <chr>, census_tra <chr>, geoid <chr>, common_nam <chr>,
## # total_popu <dbl>, population <dbl>, white_alon <dbl>, black_or_a <dbl>,
## # american_i <dbl>, asian_alon <dbl>, native_haw <dbl>, some_other <dbl>,
## # populati_2 <dbl>, hispanic_o <dbl>, not_hispan <dbl>, black_or_2 <dbl>,
## # white_al_2 <dbl>, american_2 <dbl>, asian_al_2 <dbl>, native_h_2 <dbl>,
## # some_oth_2 <dbl>, two_or_mor <dbl>, hispanic_2 <dbl>, not_hisp_2 <dbl>plot(censusDataShp, max.plot = 31)
censusDataShp[is.na(censusDataShp)] <- 0Plot our median Income by tract geometries
##
income_plot <- ggplot() +
geom_sf(data = censusDataShp, aes(fill=censusDataShp$median_inc)) +
theme_classic() +
theme_dark() +
labs(title = "Median Income for Chattanooga Tracts", fill = "Median Income")
ggplotly(income_plot)## Warning: Use of `censusDataShp$median_inc` is discouraged. Use `median_inc`
## instead.START OF MILESTONE 2 —————————————————————————————————————————————————–
Make Plot for population:
## Make Plot for population:
population_plot <- ggplot() +
geom_sf(data = censusDataShp, aes(fill=censusDataShp$total_popu)) +
theme_classic() +
theme_dark() +
labs(title = "Population for Chattanooga Tracts", fill = "Total Population")
ggplotly(population_plot)## Warning: Use of `censusDataShp$total_popu` is discouraged. Use `total_popu`
## instead.Remove the Time Stamps from the Date Incidents
##
police_incident$Date_Incident <- str_sub(police_incident$Date_Incident, 1, nchar(police_incident$Date_Incident)-12)
head(police_incident)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1200 BROAD ~ Chat~ TN 3740~ 01/01/2015 TN0330100 A9 Adam~
## 2 500 W 12TH ~ Chat~ TN 3740~ 01/01/2015 TN0330100 A6 Adam~
## 3 1000 Sherid~ Chat~ TN 3740~ 01/01/2015 TN0330100 B3 Bake~
## 4 1600 OAK ST Chat~ TN 3740~ 01/01/2015 TN0330100 B4 Bake~
## 5 4000 Blanch~ Chat~ TN 3741~ 01/01/2015 TN0330100 C2 Char~
## 6 8300 Grinde~ CHAT~ TN 3742~ 01/01/2015 TN0330100 C7 Char~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Make Incident Dates date type
##
police_incident$Date_Incident <- as.Date(police_incident$Date_Incident, format = "%m/%d/%Y")
head(police_incident)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr>
## 1 1200 BROAD ~ Chat~ TN 3740~ 2015-01-01 TN0330100 A9 Adam~
## 2 500 W 12TH ~ Chat~ TN 3740~ 2015-01-01 TN0330100 A6 Adam~
## 3 1000 Sherid~ Chat~ TN 3740~ 2015-01-01 TN0330100 B3 Bake~
## 4 1600 OAK ST Chat~ TN 3740~ 2015-01-01 TN0330100 B4 Bake~
## 5 4000 Blanch~ Chat~ TN 3741~ 2015-01-01 TN0330100 C2 Char~
## 6 8300 Grinde~ CHAT~ TN 3742~ 2015-01-01 TN0330100 C7 Char~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Plot number of incidents by date
#
police_incident_by_date <- police_incident %>% group_by(Date_Incident) %>% summarise(frequency = n())
p <- ggplot(police_incident_by_date, aes(x = police_incident_by_date$Date_Incident, y = police_incident_by_date$frequency)) + geom_col(fill="#BE93D4") + geom_smooth() + xlab("Year") + ylab("Number of Incident") + labs(title = "Police incident by Date")
ggplotly(p)## Warning: Use of `police_incident_by_date$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_date$frequency` is discouraged. Use
## `frequency` instead.## Warning: Use of `police_incident_by_date$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_date$frequency` is discouraged. Use
## `frequency` instead.## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'Plot number of incident by month
#
police_incident_by_month <- police_incident_by_date %>% group_by(Date_Incident=floor_date(Date_Incident, "month")) %>%
summarize(frequency=sum(frequency))
p <- ggplot(police_incident_by_month, aes(x = police_incident_by_month$Date_Incident, y = police_incident_by_month$frequency)) + geom_col(fill="#BE93D4") + geom_smooth() + xlab("Month") + ylab("Number of Incident") + labs(title = "Police Incident by Month")
ggplotly(p)## Warning: Use of `police_incident_by_month$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_month$frequency` is discouraged. Use
## `frequency` instead.## Warning: Use of `police_incident_by_month$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_month$frequency` is discouraged. Use
## `frequency` instead.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'Plot number of incidents by year
#
police_incident_by_year <- police_incident_by_month %>% group_by(Date_Incident=floor_date(Date_Incident, "year")) %>%
summarize(frequency=sum(frequency))
p <- ggplot(police_incident_by_year, aes(x = police_incident_by_year$Date_Incident, y = police_incident_by_year$frequency)) + geom_col(fill="#BE93D4") + geom_smooth() + xlab("Year") + ylab("Number of Incident") + labs(title = "Police Incident by Year")
ggplotly(p)## Warning: Use of `police_incident_by_year$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_year$frequency` is discouraged. Use
## `frequency` instead.## Warning: Use of `police_incident_by_year$Date_Incident` is discouraged. Use
## `Date_Incident` instead.## Warning: Use of `police_incident_by_year$frequency` is discouraged. Use
## `frequency` instead.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'Plot police incident number by incident type
#
police_incident_by_type <- police_incident %>% group_by(Incident_Type) %>% summarise(frequency = n())
police_incident_by_type$Incident_Type <- factor(police_incident_by_type$Incident_Type, levels = unique(police_incident_by_type$Incident_Type)[order(police_incident_by_type$frequency, decreasing = TRUE)])
p <- ggplot(police_incident_by_type, aes(x = police_incident_by_type$Incident_Type, y = police_incident_by_type$frequency)) + geom_col(fill="#BE93D4") + geom_smooth() + xlab("Type") + ylab("Number of Incident") + labs(title = "Police Incident by Incident Type")
ggplotly(p)## Warning: Use of `police_incident_by_type$Incident_Type` is discouraged. Use
## `Incident_Type` instead.## Warning: Use of `police_incident_by_type$frequency` is discouraged. Use
## `frequency` instead.## Warning: Use of `police_incident_by_type$Incident_Type` is discouraged. Use
## `Incident_Type` instead.## Warning: Use of `police_incident_by_type$frequency` is discouraged. Use
## `frequency` instead.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'Plot police incident number by incident description
#
police_incident_by_description <- police_incident %>% group_by(Incident_Description) %>% summarise(frequency = n())
police_incident_by_description$Incident_Description <- factor(police_incident_by_description$Incident_Description, levels = unique(police_incident_by_description$Incident_Description)[order(police_incident_by_description$frequency, decreasing = TRUE)])
p <- ggplot(police_incident_by_description, aes(x = police_incident_by_description$Incident_Description, y = police_incident_by_description$frequency)) + geom_col(fill="#BE93D4") + xlab("Description") + ylab("Number of Incident") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + labs(title = "Police Incident by Incident Description")
ggplotly(p)## Warning: Use of `police_incident_by_description$Incident_Description` is
## discouraged. Use `Incident_Description` instead.## Warning: Use of `police_incident_by_description$frequency` is discouraged. Use
## `frequency` instead.START OF MILESTONE 3 (Part of these are now milestone 5) ———————————————————————————————————————————-
Plot number of incidents by specific month for 2020
#
police_incident_by_month <- police_incident_by_date %>% group_by(Date_Incident=floor_date(Date_Incident, "month")) %>% summarize(frequency=sum(frequency))
police_incident_by_month_spec <- police_incident_by_month[police_incident_by_month$Date_Incident >= "2020-01-01" & police_incident_by_month$Date_Incident <= "2020-12-31",]
p <- ggplot(police_incident_by_month_spec, aes(x = police_incident_by_month_spec$Date_Incident, y = police_incident_by_month_spec$frequency)) + geom_col(fill="#BE93D4") + geom_smooth() + xlab("Month") + ylab("Number of Incident") + labs(title = "Police Incident for 2020 aggregated by Month")
ggplotly(p)## Warning: Use of `police_incident_by_month_spec$Date_Incident` is discouraged.
## Use `Date_Incident` instead.## Warning: Use of `police_incident_by_month_spec$frequency` is discouraged. Use
## `frequency` instead.## Warning: Use of `police_incident_by_month_spec$Date_Incident` is discouraged.
## Use `Date_Incident` instead.## Warning: Use of `police_incident_by_month_spec$frequency` is discouraged. Use
## `frequency` instead.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'Make a 2020 Police incident dataframe
##
police_incident_2020 <- police_incident[police_incident$Date_Incident >= "2020-01-01" & police_incident$Date_Incident <= "2020-12-31",]
head(police_incident_2020)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr>
## 1 7900 Chia~ Chatta~ TN 37421 2020-01-01 TN0330100 C9 Char~
## 2 800 Pine ~ Chatta~ TN 37402 2020-01-01 TN0330100 A7 Adam~
## 3 3800 Hixs~ Chatta~ TN 37343 2020-01-01 TN0330100 A1 Adam~
## 4 2200 E 26~ Chatta~ TN 37407 2020-01-01 TN0330100 B8 Bake~
## 5 100 Valle~ Chatta~ TN 37343 2020-01-01 TN0330100 A1 Adam~
## 6 1300 E 36~ Chatta~ TN 37407 2020-01-01 TN0330100 B7 Bake~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Refine Data Frame to Just show May (Highest Crime cases)
##
pi_may_2020 <- police_incident[police_incident$Date_Incident >= "2020-05-01" & police_incident$Date_Incident <= "2020-05-31",]
head(pi_may_2020)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr>
## 1 1500 Ely ~ Chatt~ TN 37343 2020-05-01 TN0330100 A3 Adam N
## 2 700 Moore~ Chatt~ TN 37411 2020-05-01 TN0330100 C2 Charl~
## 3 4600 Sunf~ Chatt~ TN 37416 2020-05-01 TN0330100 C5 Charl~
## 4 4300 Shaw~ Chatt~ TN 37411 2020-05-01 TN0330100 C2 Charl~
## 5 2300 E 5t~ Chatt~ TN 37404 2020-05-01 TN0330100 B4 Baker~
## 6 1500 Join~ Chatt~ TN 37421 2020-05-01 TN0330100 C8 Charl~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Make plotly plot for police incidents in the month of May 2020 by incident types (Milestone 5)
##
inc_map_may_2020 <- ggplot() +
geom_sf(data = censusDataShp, aes()) +
geom_point(data = pi_may_2020, aes(x=pi_may_2020$Longitude, y=pi_may_2020$Latitude, color=pi_may_2020$Incident_Type)) + labs(title="Location of Police Inicidents in May of 2020", x="Longitude", y= "Latitude", color="Incident Type") +theme(legend.position="bottom")
ggplotly(inc_map_may_2020)## Warning: Use of `pi_may_2020$Longitude` is discouraged. Use `Longitude` instead.## Warning: Use of `pi_may_2020$Latitude` is discouraged. Use `Latitude` instead.## Warning: Use of `pi_may_2020$Incident_Type` is discouraged. Use `Incident_Type`
## instead.Refine DataFrame to show only part one crimes in may 2020
##
pi_type1_crimes_2020 <- pi_may_2020 %>% filter(pi_may_2020$Incident_Type == "Part 1 Crimes")Make Plotly plot for all part one police incidents in the month of may in 2020 (Milestone 5)
##
p1_may_map <- ggplot() +
geom_sf(data = censusDataShp, aes()) +
geom_point(data = pi_type1_crimes_2020, aes(x=pi_type1_crimes_2020$Longitude, y=pi_type1_crimes_2020$Latitude, color=pi_type1_crimes_2020$Incident_Description)) + labs(title="Location of Part 1 Police Inicidents in May of 2020", x="Longitude", y= "Latitude", color="Incident Description") +theme(legend.position="bottom")
ggplotly(p1_may_map)## Warning: Use of `pi_type1_crimes_2020$Longitude` is discouraged. Use `Longitude`
## instead.## Warning: Use of `pi_type1_crimes_2020$Latitude` is discouraged. Use `Latitude`
## instead.## Warning: Use of `pi_type1_crimes_2020$Incident_Description` is discouraged. Use
## `Incident_Description` instead.Refine DataFrame to show only robbery crimes in may 2020
##
p1_Crimes_may_2020_robbery <- pi_may_2020 %>% filter(pi_may_2020$Incident_Description == "Robbery")
head(p1_Crimes_may_2020_robbery)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr>
## 1 3500 Brai~ Chatta~ TN 37411 2020-05-01 TN0330100 C2 Char~
## 2 5100 Hwy ~ Chatta~ TN 37343 2020-05-03 TN0330100 A2 Adam~
## 3 6300 Stoc~ Chatta~ TN 37416 2020-05-04 TN0330100 C5 Char~
## 4 3200 12th~ Chatta~ TN 37407 2020-05-04 TN0330100 B8 Bake~
## 5 7600 E Br~ Chatta~ TN 37421 2020-05-06 TN0330100 C8 Char~
## 6 7000 Mccu~ Chatta~ TN 37421 2020-05-08 TN0330100 C9 Char~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>Make Plotly Plot to show where robberies occured in May 2020 (Milestone 5)
##
p1_may_map_robbery <- ggplot() +
geom_sf(data = censusDataShp, aes()) +
geom_point(data = p1_Crimes_may_2020_robbery, aes(x=p1_Crimes_may_2020_robbery$Longitude, y=p1_Crimes_may_2020_robbery$Latitude, color=p1_Crimes_may_2020_robbery$Incident_Description)) + labs(title="Location of Robberies in May of 2020", x="Longitude", y= "Latitude", color="Incident Type")
ggplotly(p1_may_map_robbery)## Warning: Use of `p1_Crimes_may_2020_robbery$Longitude` is discouraged. Use
## `Longitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Latitude` is discouraged. Use
## `Latitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Incident_Description` is
## discouraged. Use `Incident_Description` instead.Start of Milestone 4 ————————————————————————————————————————————————————————
Make a plot overlaying the shapefile population data with the amounts of Part one crimes
##
p <- ggplot() +
geom_sf(data = censusDataShp, aes(fill=censusDataShp$total_popu)) +
geom_point(data = p1_Crimes_may_2020_robbery, aes(x=p1_Crimes_may_2020_robbery$Longitude, y=p1_Crimes_may_2020_robbery$Latitude, color=p1_Crimes_may_2020_robbery$Incident_Description)) + labs(title="Location of Robberies in May of 2020", x="Longitude", y= "Latitude", color="Incident Type", fill= "Population")
ggplotly(p)## Warning: Use of `censusDataShp$total_popu` is discouraged. Use `total_popu`
## instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Longitude` is discouraged. Use
## `Longitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Latitude` is discouraged. Use
## `Latitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Incident_Description` is
## discouraged. Use `Incident_Description` instead.Make a plot overlaying the shapefile income data with the amounts of Part one crimes
##
p <- ggplot() +
geom_sf(data = censusDataShp, aes(fill=censusDataShp$median_inc)) +
geom_point(data = p1_Crimes_may_2020_robbery, aes(x=p1_Crimes_may_2020_robbery$Longitude, y=p1_Crimes_may_2020_robbery$Latitude, color=p1_Crimes_may_2020_robbery$Incident_Description)) + labs(title="Location of Robberies in May of 2020", x="Longitude", y= "Latitude", color="Incident Type", fill= "Median Income")
ggplotly(p)## Warning: Use of `censusDataShp$median_inc` is discouraged. Use `median_inc`
## instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Longitude` is discouraged. Use
## `Longitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Latitude` is discouraged. Use
## `Latitude` instead.## Warning: Use of `p1_Crimes_may_2020_robbery$Incident_Description` is
## discouraged. Use `Incident_Description` instead.Make new dataframe for p1_Crimes_may_2020_shoplifting
##
#Shoplifting
p1_Crimes_may_2020_shoplifting <- pi_may_2020 %>% filter(pi_may_2020$Incident_Description == "Shoplifting")
head(p1_Crimes_may_2020_shoplifting)## # A tibble: 6 x 19
## Address City State Zip Date_Incident Jurisdiction Incident_Tract Zone
## <chr> <chr> <chr> <chr> <date> <chr> <chr> <chr>
## 1 400 Green~ Chatta~ TN 37411 2020-05-01 TN0330100 C3 Char~
## 2 3500 Cumm~ Chatta~ TN 37419 2020-05-01 TN0330100 A13 Adam~
## 3 2000 Mcca~ Chatta~ TN 37404 2020-05-01 TN0330100 B5 Bake~
## 4 1300 E 3r~ Chatta~ TN 37404 2020-05-02 TN0330100 B4 Bake~
## 5 5900 High~ Chatta~ TN 37343 2020-05-02 TN0330100 A1 Adam~
## 6 1300 E 3r~ Chatta~ TN 37404 2020-05-02 TN0330100 B4 Bake~
## # ... with 11 more variables: UCR_Incident_Code <chr>,
## # Incident_Description <chr>, Incident_Type <chr>, Case_Number <chr>,
## # Case_Status <chr>, Case_Status_Description <chr>, Latitude <dbl>,
## # Neighborhood <chr>, Longitude <dbl>, Location <chr>, citydst <dbl>#combined dataframe for robbery and shoplifting
p1_shoplift_robbery <- rbind(p1_Crimes_may_2020_robbery,p1_Crimes_may_2020_shoplifting)Plot for location of Robberies and shoplifting incidents based with median income (Milestone 5)
##
p <- ggplot() + geom_sf(data = censusDataShp, aes(fill=censusDataShp$median_inc)) +
geom_point(data = p1_shoplift_robbery, aes(x=p1_shoplift_robbery$Longitude, y=p1_shoplift_robbery$Latitude, color= p1_shoplift_robbery$Incident_Description))+
labs(title="Location of Robberies and Shoplifting incidents in May of 2020", x="Longitude", y= "Latitude", color="Incident Description", fill= "Median Income")
ggplotly(p)## Warning: Use of `censusDataShp$median_inc` is discouraged. Use `median_inc`
## instead.## Warning: Use of `p1_shoplift_robbery$Longitude` is discouraged. Use `Longitude`
## instead.## Warning: Use of `p1_shoplift_robbery$Latitude` is discouraged. Use `Latitude`
## instead.## Warning: Use of `p1_shoplift_robbery$Incident_Description` is discouraged. Use
## `Incident_Description` instead.Graph Showing Locations of Robberies and Shoplifting based on population density (Milestone 5)
##Graph Showing Locations of Robberies and Shoplifting based on population density (Milestone 5)
p <- ggplot() +
geom_sf(data = censusDataShp, aes(fill=censusDataShp$total_popu)) +
geom_point(data = p1_shoplift_robbery, aes(x=p1_shoplift_robbery$Longitude, y=p1_shoplift_robbery$Latitude, color= p1_shoplift_robbery$Incident_Description))+
labs(title="Location of Robberies and Shoplifting incidents in May of 2020", x="Longitude", y= "Latitude", color="Incident Description", fill= "Population")
ggplotly(p)## Warning: Use of `censusDataShp$total_popu` is discouraged. Use `total_popu`
## instead.## Warning: Use of `p1_shoplift_robbery$Longitude` is discouraged. Use `Longitude`
## instead.## Warning: Use of `p1_shoplift_robbery$Latitude` is discouraged. Use `Latitude`
## instead.## Warning: Use of `p1_shoplift_robbery$Incident_Description` is discouraged. Use
## `Incident_Description` instead.